Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and sh...
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MDPI AG
2021
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oai:doaj.org-article:c96b736ec43c41df9a58407429dbf8792021-11-25T18:04:49ZDeep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication10.3390/jmse91112522077-1312https://doaj.org/article/c96b736ec43c41df9a58407429dbf8792021-11-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1252https://doaj.org/toc/2077-1312A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10<sup>−3</sup> can be obtained at a signal-to-noise ratio of −8 dB.Yufei LiuFeng ZhouGang QiaoYunjiang ZhaoGuang YangXinyu LiuYinheng LuMDPI AGarticlecyclic shift keying spread spectrumlow signal-to-noise ratiomultipath effectsneural network modellong- and short-term memoryNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1252, p 1252 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
cyclic shift keying spread spectrum low signal-to-noise ratio multipath effects neural network model long- and short-term memory Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 |
| spellingShingle |
cyclic shift keying spread spectrum low signal-to-noise ratio multipath effects neural network model long- and short-term memory Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Yufei Liu Feng Zhou Gang Qiao Yunjiang Zhao Guang Yang Xinyu Liu Yinheng Lu Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| description |
A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10<sup>−3</sup> can be obtained at a signal-to-noise ratio of −8 dB. |
| format |
article |
| author |
Yufei Liu Feng Zhou Gang Qiao Yunjiang Zhao Guang Yang Xinyu Liu Yinheng Lu |
| author_facet |
Yufei Liu Feng Zhou Gang Qiao Yunjiang Zhao Guang Yang Xinyu Liu Yinheng Lu |
| author_sort |
Yufei Liu |
| title |
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_short |
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_full |
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_fullStr |
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_full_unstemmed |
Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication |
| title_sort |
deep learning-based cyclic shift keying spread spectrum underwater acoustic communication |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/c96b736ec43c41df9a58407429dbf879 |
| work_keys_str_mv |
AT yufeiliu deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication AT fengzhou deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication AT gangqiao deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication AT yunjiangzhao deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication AT guangyang deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication AT xinyuliu deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication AT yinhenglu deeplearningbasedcyclicshiftkeyingspreadspectrumunderwateracousticcommunication |
| _version_ |
1718411707031748608 |